{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6266a327",
   "metadata": {},
   "source": [
    "## Quick Start\n",
    "\n",
    "To get started with *pymgrid*, first [clone or install](../../html/getting_started.html) the package.\n",
    "\n",
    "This notebook shows how to define a simple microgrid, create actions to control it, and read the results. Microgrids can be defined by either defining a set of modules and then passing them to the ``Microgrid`` constructor or by a YAML config file. We detail the first case here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6516d304",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "np.random.seed(0)\n",
    "\n",
    "from pymgrid import Microgrid\n",
    "from pymgrid.modules import (\n",
    "    BatteryModule,\n",
    "    LoadModule,\n",
    "    RenewableModule,\n",
    "    GridModule)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00673154",
   "metadata": {},
   "source": [
    "### Defining a Microgrid"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d57b8952",
   "metadata": {},
   "source": [
    "We can then define some components of our microgrid. We will define two batteries, one with a capacity of 100 kWh and another with a slower charging rate and lower efficiency but a capacity of 1000 kWh."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "53c9221a",
   "metadata": {},
   "outputs": [],
   "source": [
    "small_battery = BatteryModule(min_capacity=10,\n",
    "                              max_capacity=100,\n",
    "                              max_charge=50,\n",
    "                              max_discharge=50,\n",
    "                              efficiency=0.9,\n",
    "                              init_soc=0.2)\n",
    "\n",
    "large_battery = BatteryModule(min_capacity=10,\n",
    "                              max_capacity=1000,\n",
    "                              max_charge=10,\n",
    "                              max_discharge=10,\n",
    "                              efficiency=0.7,\n",
    "                              init_soc=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3b248b0",
   "metadata": {},
   "source": [
    "We will also define a load and photovoltaic (pv) module -- the latter representing, say, a solar farm -- with some random data. We will define 90 days worth of data, in hourly increments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "75d6d6df",
   "metadata": {},
   "outputs": [],
   "source": [
    "load_ts = 100+100*np.random.rand(24*90) # random load data in the range [100, 200].\n",
    "pv_ts = 200*np.random.rand(24*90) # random pv data in the range [0, 200].\n",
    "\n",
    "load = LoadModule(time_series=load_ts)\n",
    "\n",
    "pv = RenewableModule(time_series=pv_ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1739938",
   "metadata": {},
   "source": [
    "Finally, we define an external electrical grid to fill any energy gaps. \n",
    "\n",
    "The grid time series must contain three or four columns. The first three denote import price, export price, and carbon dioxide production per kWh. If a fourth column exists, it denotes the grid status (as a bool); if it does not, the grid is assumed to always be up and running."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2c250ad4",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_ts = [0.2, 0.1, 0.5] * np.ones((24*90, 3))\n",
    "\n",
    "grid = GridModule(max_import=100,\n",
    "                  max_export=100,\n",
    "                  time_series=grid_ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c0c94b0",
   "metadata": {},
   "source": [
    "We can then pass these to the ``Microgrid`` constructor to define a microgrid. Here, we give our renewable module the name \"pv\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3f584c56",
   "metadata": {},
   "outputs": [],
   "source": [
    "modules = [\n",
    "    small_battery, \n",
    "    large_battery,\n",
    "    ('pv', pv),\n",
    "    load,\n",
    "    grid]\n",
    "\n",
    "microgrid = Microgrid(modules)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9b42b83",
   "metadata": {},
   "source": [
    "Printing the microgrid will tell us the modules contained in that microgrid. By default a ``balancing`` module is added to keep track of any unmet demand or excess production. This can be disabled by passing ``unbalanced_energy_module=False``, but is not recommended."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e893b0a4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Microgrid([load x 1, pv x 1, balancing x 1, battery x 2, grid x 1])\n"
     ]
    }
   ],
   "source": [
    "print(microgrid)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf68ba4e",
   "metadata": {},
   "source": [
    "We can then access the modules in the microgrid by name or by key:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d45899bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[RenewableModule(time_series=<class 'numpy.ndarray'>, raise_errors=False, forecaster=NoForecaster, forecast_horizon=0, forecaster_increase_uncertainty=False, provided_energy_name=renewable_used)]\n"
     ]
    }
   ],
   "source": [
    "print(microgrid.modules.pv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6bcf9b54",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "print(microgrid.modules.grid is microgrid.modules['grid'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3eb58cd",
   "metadata": {},
   "source": [
    "### Controlling a microgrid\n",
    "\n",
    "You must pass an action for each ``controllable`` module to control the microgrid. The fixed modules are stored in the attribute ``microgrid.controllable``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a360f3dc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{\n",
       "  \"battery\": \"[BatteryModule(min_capacity=10, max_capacity=100, max_charge=50, max_discharge=50, efficiency=0.9, battery_cost_cycle=0.0, battery_transition_model=None, init_charge=None, init_soc=0.2, raise_errors=False), BatteryModule(min_capacity=10, max_capacity=1000, max_charge=10, max_discharge=10, efficiency=0.7, battery_cost_cycle=0.0, battery_transition_model=None, init_charge=None, init_soc=0.2, raise_errors=False)]\",\n",
       "  \"grid\": \"[GridModule(max_import=100, max_export=100)]\"\n",
       "}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "microgrid.controllable"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08cd3053",
   "metadata": {},
   "source": [
    "As we can see, our \"load\", \"battery\", and \"grid\" modules are fixed.\n",
    "\n",
    "We can also view what modules we need to pass an action for by getting an empty action from the microgrid. Here, all ``None`` values must be replaced to pass this action as a control."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2d94930f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'battery': [None, None], 'grid': [None]}\n"
     ]
    }
   ],
   "source": [
    "print(microgrid.get_empty_action())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3337709",
   "metadata": {},
   "source": [
    "We can then simply define a control for these modules. Before doing so, we will reset our microgrid and then check its current state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "117a7ae8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>load</th>\n",
       "      <th>0</th>\n",
       "      <th>load_current</th>\n",
       "      <td>-169.646919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pv</th>\n",
       "      <th>0</th>\n",
       "      <th>renewable_current</th>\n",
       "      <td>65.358668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">battery</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">0</th>\n",
       "      <th>soc</th>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>current_charge</th>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
       "      <th>soc</th>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>current_charge</th>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">grid</th>\n",
       "      <th rowspan=\"4\" valign=\"top\">0</th>\n",
       "      <th>import_price_current</th>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>export_price_current</th>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>co2_per_kwh_current</th>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grid_status_current</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         0\n",
       "load    0 load_current         -169.646919\n",
       "pv      0 renewable_current      65.358668\n",
       "battery 0 soc                     0.200000\n",
       "          current_charge         20.000000\n",
       "        1 soc                     0.200000\n",
       "          current_charge        200.000000\n",
       "grid    0 import_price_current    0.200000\n",
       "          export_price_current    0.100000\n",
       "          co2_per_kwh_current     0.500000\n",
       "          grid_status_current     1.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "microgrid.reset()\n",
    "microgrid.state_series.to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b4d2638",
   "metadata": {},
   "source": [
    "We will attempt to meet our load demand of 169.646919 by using our available renewable and then discharging our batteries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c2c64cfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "load = -1.0 * microgrid.modules.load.item().current_load\n",
    "pv = microgrid.modules.pv.item().current_renewable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "79b1ef8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "net_load = load + pv # negative if load demand exceeds pv\n",
    "\n",
    "if net_load > 0:\n",
    "    net_load = 0.0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "190fe39c",
   "metadata": {},
   "source": [
    "For our batteries, we will attempt to generate the lower of the excess load and the maximum production."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8424911a",
   "metadata": {},
   "outputs": [],
   "source": [
    "battery_0_discharge = min(-1*net_load, microgrid.modules.battery[0].max_production)\n",
    "net_load += battery_0_discharge\n",
    "\n",
    "battery_1_discharge = min(-1*net_load, microgrid.modules.battery[1].max_production)\n",
    "net_load += battery_1_discharge"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90700c09",
   "metadata": {},
   "source": [
    "Finally, we will let our grid clean up the rest -- or as must as it can."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6aa7b085",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "grid_import = min(-1*net_load, microgrid.modules.grid.item().max_production)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "027419ac",
   "metadata": {},
   "source": [
    "Putting this together, we have our control. \n",
    "\n",
    "**Note that positive values denote energy moving into the microgrid and negative values denote energy leaving the microgrid.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e2ed2c6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'battery': [9.0, 7.0], 'grid': [88.28825060785877]}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "control = {\"battery\" : [battery_0_discharge, battery_1_discharge],\n",
    "           \"grid\": [grid_import]}\n",
    "\n",
    "control"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36681d36",
   "metadata": {},
   "source": [
    "We can then run the microgrid with this control. Since this control is not normalized, we pass ``normalized=False``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d2212f7b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "obs, reward, done, info = microgrid.run(control, normalized=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df844499",
   "metadata": {},
   "source": [
    "### Analyzing Results\n",
    "\n",
    "After passing a control to the microgrid, we can view the results by viewing the microgrid's -- or any of the modules -- logs. \n",
    "\n",
    "The microgrid's log has one row for each action taken. There are values for both the actions -- e.g. the amount of load met -- as well as the state: for example, the current load. \n",
    "\n",
    "**Note that the state values are the values of the state from *before* the action was taken.**\n",
    "\n",
    "The columns are a ``pd.MultiIndex``, with three levels: module names, module name enumeration (e.g. which number of each module name we are in) and property.\n",
    "\n",
    "For example, since there is one ``load``, all of its log entries will be available under the key ``('load', 0)``. Since there are two batteries, there will be both ``(battery, 0)`` and ``(battery, 1)``."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5f570ef",
   "metadata": {},
   "source": [
    "We can see that we met all of our load:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "26121b4f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>module_name</th>\n",
       "      <th colspan=\"3\" halign=\"left\">load</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>module_number</th>\n",
       "      <th colspan=\"3\" halign=\"left\">0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>field</th>\n",
       "      <th>reward</th>\n",
       "      <th>load_met</th>\n",
       "      <th>load_current</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>169.646919</td>\n",
       "      <td>-169.646919</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "module_name     load                         \n",
       "module_number      0                         \n",
       "field         reward    load_met load_current\n",
       "0                0.0  169.646919  -169.646919"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "microgrid.log.loc[:, pd.IndexSlice['load', 0, :]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56e7e758",
   "metadata": {},
   "source": [
    "And consumed all available pv, even though we did not define a control for it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "71ac1673",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr>\n",
       "      <th>module_name</th>\n",
       "      <th colspan=\"4\" halign=\"left\">pv</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>module_number</th>\n",
       "      <th colspan=\"4\" halign=\"left\">0</th>\n",
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       "    <tr>\n",
       "      <th>field</th>\n",
       "      <th>reward</th>\n",
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       "    </tr>\n",
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      ],
      "text/plain": [
       "module_name       pv                                             \n",
       "module_number      0                                             \n",
       "field         reward curtailment renewable_used renewable_current\n",
       "0                0.0         0.0      65.358668         65.358668"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "microgrid.log.loc[:, pd.IndexSlice['pv', 0, :]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0ddf53e",
   "metadata": {},
   "source": [
    "Since we have two batteries, we will have log entries for each of them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7c032938",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>module_number</th>\n",
       "      <th colspan=\"5\" halign=\"left\">0</th>\n",
       "      <th colspan=\"5\" halign=\"left\">1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>field</th>\n",
       "      <th>reward</th>\n",
       "      <th>discharge_amount</th>\n",
       "      <th>charge_amount</th>\n",
       "      <th>soc</th>\n",
       "      <th>current_charge</th>\n",
       "      <th>reward</th>\n",
       "      <th>discharge_amount</th>\n",
       "      <th>charge_amount</th>\n",
       "      <th>soc</th>\n",
       "      <th>current_charge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>20.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "module_number      0                                                     \\\n",
       "field         reward discharge_amount charge_amount  soc current_charge   \n",
       "0               -0.0              9.0           0.0  0.2           20.0   \n",
       "\n",
       "module_number      1                                                     \n",
       "field         reward discharge_amount charge_amount  soc current_charge  \n",
       "0               -0.0              7.0           0.0  0.2          200.0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "microgrid.log.loc[:, 'battery']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6402dc5c",
   "metadata": {},
   "source": [
    "### Plotting Results\n",
    "\n",
    "We can also utilize pandas plotting functionality to view results. To illustrate this, we will run the microgrid for an additional ten steps with randomly sampled actions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "298cbc0a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "for _ in range(10):\n",
    "    microgrid.run(microgrid.sample_action(strict_bound=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a5c2a92",
   "metadata": {},
   "source": [
    "Here we will plot the load met, pv consumed and loss load:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "603c4f14",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "microgrid.log[[('load', 0, 'load_met'), \n",
    "               ('pv', 0, 'renewable_used'),\n",
    "               ('balancing', 0, 'loss_load')]].droplevel(axis=1, level=1).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46e9b61b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}